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Satellite image retrieval of random forest (rf-PNN) based probablistic neural network

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Abstract

The image retrieval process grows a massive problem due to the huge number of data exist on the web. So, the accurate retrieval of satellite image of the given query is one of the necessary requirement. The paper propose a classifier of Probabilistic Neural Network based Random Forest (rf-PNN), which is retrieving an exact match of a classified data as per user’s need. Various techniques of Adaptive Median Filter (for pre-processing), Discrete Cosine Transform based Discrete Orthogonal Stockwell Transform (for segmentation) and Linear Binary Pattern (for feature extraction) are presented to process the trained dataset as well as given query. Then, both the feature extracted samples are assigned to compare with the classified network. The experimental setup is demonstrated on MATLAB tool. Then the relevant feature retrieval are analyze under the performance measures of 92% accurate rate, sensitivity for 89.25%, specificity for 94.1% and precision at a rate of 90.08%.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, India

    N. Bharatha Devi

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  1. N. Bharatha Devi
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Correspondence to N. Bharatha Devi.

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Communicated by: H. Babaie

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Bharatha Devi, N. Satellite image retrieval of random forest (rf-PNN) based probablistic neural network. Earth Sci Inform 15, 941–949 (2022). https://doi.org/10.1007/s12145-021-00759-3

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  • DOI: https://doi.org/10.1007/s12145-021-00759-3

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